Skillsoft Blog

Today’s corporate learning environments for technology and developer professionals revolve around areas needed for training, such as specific technologies or certifications. However, as our understanding of technology evolves, so do the job roles that utilize those technologies. According to a World Economic report, “On average, by 2020, more than a third of the desired core skill sets of most occupations will be comprised of skills that are not yet considered crucial to the job today.”

Ultimately, this leads to the subject of the shortage of talent, and in particular the shortfall of data scientists. A recent poll found that the median tenure of a data scientist is just two years or less, although that number appears to be growing as many now plan to stay for at least three years. Part of the reason is that the demand far outweighs the supply, with Bloomberg estimating that job postings for data scientists rose 75% from 2015 to 2018.

Is data the new oil?

In some circles, yes data is considered the “new oil” because solid data is essential to stay competitive in today’s rapidly evolving market. In one recent study, 85% of executives said they believe that artificial intelligence (AI) is essential to obtaining or sustaining a competitive advantage while 81% admitted they do not understand the data required for AI and 80% classified their data as inaccessible, untrusted or unanalyzed. Building a reliable foundation of data for transformational and innovative technologies like machine learning or AI, is critical for companies wanting to develop better experiences for their customers.

The vital role data scientists play in business

Almost every organization shares this same urgent need. When faced with a skills gap in their teams and stiff competition in recruiting, how can organizations effectively build a data science team? One way is to start by looking at your data analysts. Data analysts are often swimming in disparate, federated data sources, operating on legacy databases, working with mixed data sources, and using Excel to do things it was never intended to do. Their systems are brittle because of this, and they don’t scale well and can be less than secure.

However, what if you took this data analyst on a journey to become a data scientist, someone who can partner with business teams to reimagine and evolve business processes, create a reactive data culture with a focus on reporting over analytics and allow for the adoption of innovative technologies?

With Skillsoft Aspire learning journeys, that’s precisely what we aim to do. We take roles commonly found in organizations today and provide a sequenced path of instruction that moves a learner towards an aspirational role. These intentionally designed journeys are not simply curated learning paths assembled because there is a relationship, but journeys that build upon each other and contain new content created specifically for the critical skills required for each aspirational role.

The Data Analyst to Data Scientist journey takes a leaner on a path that starts with courses covering areas that data analysts typically are involved with on a day to day basis. Topics such as Python, R, architecture, and statistics that help them progress to the next role are then introduced. By the end of the 90+ hour journey, the learner will have worked up to visualization, APIs, machine learning and deep learning algorithms. To ensure anyone taking the journey fully understands what is necessary for their new role, we have plenty of assessments (more than 100 per track and each journey has four tracks) and hands-on practice labs (done virtually on real equipment/applications) so each learner can demonstrate their knowledge and applicability of the material covered. At the culmination of the journey, users must pass a rigorous final exam and complete a capstone project to earn their credential.

The role of data scientist also requires someone who is not only technically excellent but also has the business and leadership skills to communicate, present, manage projects and foster active collaboration with other areas of the business. So that learners can deliver impactful projects as data scientists, leadership and communication training is integrated into each journey to ensure a more holistic framework. We also offer the option of including some of our productivity and collaboration tools content. We believe a well-rounded data scientist is a strong asset for any company trying to innovate, head off disruption, or become or maintain a market leader status.

We aim to build and make available by the end of the year at least eight specific journeys around Data Science, Machine Learning, AI, Security, and DevOps. Each journey comprises at least 50-80 hours of courses plus optional multimodal content like videos and books, plus an additional 10-12 hours of practice labs, certification prep, and assessments. Percipio customers with access to Skillsoft’s Technology and Developer content library automatically receive all Skillsoft Aspire learning journeys.